Improved noninvasive diagnosis of acute pulmonary embolism with optimally selected clinical and chest radiographic findings

Abstract

Rationale and objectives: The authors improved the noninvasive diagnosis of acute pulmonary embolism (PE) by studying the clinical and chest radiographic findings of patients suspected of having PE and correlating those findings with the physicians' clinical impression.

Methods: A stepwise linear discriminant algorithm was developed on the basis of 1,064 patients from the Prospective Investigation of Pulmonary Embolism Diagnosis (PIOPED) study to select clinical and chest radiographic findings with the highest diagnostic power in patients suspected of having PE. Subsequently, a linear classifier and a nonlinear artificial neural network were developed to help diagnose PE on the basis of the reduced number of findings.

Results: Both classifiers produced a statistically significant improvement (Az = 0.77 +/- 0.02) in the clinical performance of the PIOPED physicians (Az = 0.72 +/- 0.02). Results are also presented separately for groups of patients classified on the basis of the difficulty level of their ventilation-perfusion lung scans.

Conclusion: Two computer-aided diagnostic tools were developed to assist physicians in the assessment of the pretest likelihood of PE by using an optimally reduced number of findings.